What's the Best Way to Scale AI in Reinsurance Claims Ops?
It's so interesting that the reinsurance industry has historically been viewed as slow to adopt broad digital technologies, yet its embrace of AI has been remarkably rapid. Despite the high rate of adoption, few reinsurers have successfully translated early experimentation into any kind of actual sustained enterprise value. This significant gap between technological engagement and realised business utility signals quite the bottleneck that must be addressed by senior leadership.
Here’s what I’m proposing, based on experience and backed by research: modular AI architecture.
Modular vs. Monolithic
I want to start my explanation of what modular AI means in reinsurance with an explanation of its alternative architecture, monolithic. Because, essentially, the architectural choice between monolithic and modular systems determines the reinsurer’s capacity for future AI integration and scaling. Decerto explains it well, stating that monolithic systems are constrained by their integrated design, which in turn limits customisation and makes integrating technologies, AI and such, difficult and expensive.
Conversely, modular architecture is designed specifically for high customisation and adaptability. By breaking the system into specialised components, each can be independently developed and updated. This flexibility is a winner for long-term relevance. It really seals the deal in the enablement of systems to rapidly integrate new AI capabilities.
What This Means For Reinsurance Claims
The way I see it, claims management offers the single greatest immediate area of impact for modular AI integration, given the high volume of transactions and repetitive processes involved. You know, you’ve got your legacy systems and all the difficulties they bring organisations these days, and then you’ve got companies integrating AI systems modularly, instantly making use of the ability to put vast quantities of unstructured data to work in order to dynamically learn and innovate.
The modular architecture means that you can mix and match where appropriate without your platform ending up a hot mess of incompatible systems. Reinsurers can combine various specialised modules from visual damage assessment to focused subrogation engines and core operations suites to create a tailored system that delivers measurable results across the value chain.
This is a very proactive shift that perhaps not many organisations feel ready for, but if efficiency is what you’re looking for, modular architecture is a great way to go. Recent studies show that machine learning reduces claim review time by up to 70%, and accuracy frequently improves to over 95%. These results speak for themselves.
How Reinsurers Can Get Started
It’s not breaking news that phased approaches are often preferred, especially with big companies. As Gradient AI puts it, an incremental approach to anything involving mass change, like technology implementation, helps to prevent the dreaded disruption and legacy system overhaul failures.
If I were to suggest completely broad phases for incremental change using a modular AI architecture, this is probably how I’d go about it:
Phase 1: Readiness
The first action is to assess how ready your organisation is. Look at aspects like existing system constraints, the quality and accessibility of internal data, and the current maturity of team capabilities.
Modular AI really does rely on the quality of that early set foundation of data, so this isn’t a step to be missed, even though many are tempted. But if you take into account the fact that poor data quality and fragmented systems hinder a majority of AI projects, maybe you’ll be tempted once more to think again!
Phase 2: Pilot
When it is decided that modular architecture will be used and data is prepared for this, organisations can then proceed to deployment.
A specific process should be selected for a pilot program. High transactional is best, and ideal candidates could be complex document analysis or validation of reinsurance bordereaux. Crucially, the pilot must define and establish clear success metrics upfront.
Phase 3: Integration and scaling
Roll-out is an ongoing process, whether you like to think of it as so or not. This is because in the world of technology (and also in the world of claims!) things change often. You must monitor and refine continuously to ensure long-term value.
Do this with KPIs, and also through the governance processes, which you will have set up at the beginning of phase 1. Although plot twist, this also may need refining from time to time, too. What was it I said about proactivity again? 😅
Yes, it is hard work. But to do it in a roundabout way would be an injustice to your organisation and its AI success. The chasm between high AI adoption and low realised value is fundamentally an architectural failure, not a technological one. The modular approach offers organisations a pivot to a de-risked, scalable pathway that directly links AI deployment to your core financial objectives.
I’ve been guiding Senior Leadership and IT teams on their digital implementation journeys for over 27 years now, offering practical support and 'been there and done it' expertise to set them up for success.
If you’d like support in furthering your technology implementation in reinsurance claims or accounting functions, get in touch at svenscandella@mac.com
Sources:
https://www.decerto.com/post/comparing-monolithic-and-modular-architectures-in-insurance-software
https://blog.talli.ai/claims-industry-statistics/
https://www.gradientai.com/pc-blog-whats-next-for-ai-in-insurance-6-trends-to-watch-in-2025
https://medium.com/@digicore/modernizing-insurance-a-complete-roadmap-for-going-digital-083c91cea333